codexctl

One CLI. Agent commands print JSON on stdout (exit 0 on a completed turn, 2 otherwise); plain calls print the model's text on stdout and a stats line on stderr:

node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js <command> [options] "prompt"

Run with no arguments for full usage. Working directory defaults to the current one; pass --cwd.

Mental model

• Thread = persistent conversation with a model, cwd, sandbox, approval policy. Turn = one prompt→completion cycle producing items (agentMessage, commandExecution, fileChange, reasoning). Threads persist on disk under ~/.codex/sessions and can be resumed, forked, reverted.
• The app server asks the client to approve commands (server→client requests). codexctl auto-answers with --decide (default accept). Safety comes from the sandbox, not from the approval answer: workspace-write (default, writes only inside cwd, no network), read-only, danger-full-access.
• Each invocation normally spawns a private app-server. Run codexctl serve once to get a shared WebSocket server; then invocations attach to it, and steer/interrupt can reach turns started by earlier invocations.
• Two kinds of call. Agent commands (run, resume, fork, review) use Codex's own agent prompt and tools in a workspace. Plain commands (ask, compare, map) replace that prompt with ours (assets/plain.md, or --system/--instructions), strip skills, plugins and connectors, run read-only in an ephemeral thread, and cost ~7 KB per request. Any model works for either kind.

Common invocations

# delegate a task (Luna, low effort, writes inside cwd, no prompts)
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js run --cwd . "Add unit tests for src/parser.ts and run them"

# stronger model / more effort, read-only investigation
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js run --model gpt-5.6-sol --effort high --sandbox read-only "Explain how auth works in this repo"

# continue a thread, fork it into another model, just the text
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js resume THREAD_ID "Now handle the edge case for empty input" --text
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js fork THREAD_ID --model gpt-5.6-terra "Second opinion: is the previous fix correct?"

# Codex code review of uncommitted changes (or --base main / --commit SHA / --custom "...")
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js review --cwd .

# structured answer
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js run --schema schema.json --text "Classify each file in src/ as core or util"

# free OpenRouter models as the agent ($0; chain falls through on 429/provider error; lean prompt is automatic)
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js run --model free --cwd . "Add a --dry-run flag to bin/cleanup.py and run its tests"
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js run --model "deepseek,nemotron-free" --cwd . "..."

# plain calls: one model, our instructions, no agent prompt
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js ask "Rewrite this paragraph in plain English: ..."          # default: flash (Gemini 3.8 Flash)
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js ask free --file notes.md "Reformat as a markdown table"       # $0 chain, fallback on 429
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js ask deepseek --file big.log "List every error class with counts"   # cheap 1M-context model
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js ask --system "You are a copy editor." sonnet --prompt-file brief.md

# A/B/C prose variants for the user to pick from (spark = Muse Spark, flash = Gemini Flash, sonnet); --blind, --judge default
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js compare "Write a launch tweet for ... Plain, no hashtags."
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js compare --models spark,flash --n 2 --blind --judge default "..."
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js reveal latest

# per-section output over a huge file, chunks in parallel, cheap model with a free floor
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js map --model cheap-chain --chunk 150000 --label --file transcript.txt "Extract every decision made, as bullets" --out decisions.md

# long task in the background: returns {pid, journal, resultFile}; poll resultFile
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js serve
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js run --detach --model gpt-5.6-sol --effort medium "Port the build to pnpm workspaces"
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js steer THREAD_ID "Also update the CI workflow"
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js interrupt THREAD_ID

# inventory / housekeeping
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js models        # Codex models
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js aliases       # alias -> OpenRouter id, chains, groups
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js usage         # spend of plain calls, last 30 days
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js limits
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js list --cwd . --limit 10
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js show THREAD_ID
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js delete THREAD_ID ...
node ${CLAUDE_SKILL_DIR}/scripts/codexctl.js exec --cwd . -- python -m pytest -q

Choosing model and effort

need	pick
quick edits, tests, scripts, throwaway probes	gpt-5.6-luna --effort low (default for run)
medium refactors, reviews	gpt-5.6-terra --effort medium
harder reasoning, second opinions	gpt-5.6-sol --effort high
the user's default heavy model	gpt-6-astra (efforts up to ultra; expensive)
$0 agent work on throwaway or public code, no ChatGPT quota	run --model free
menial text work: summaries, extraction, reformatting, classification, test data	ask free first, then ask cheap-chain (DeepSeek → Qwen → GLM → free)
prose the user will publish: tweets, copy, articles	compare (group prose = spark, flash, sonnet); show A/B/C, let the user pick
big inputs	whole-file ask deepseek --file F when it fits (cheap models take 1M tokens); map for per-section output
a quick general answer	ask (flash)

On short tasks effort mostly adds latency, not quality. aliases lists every alias; any org/model OpenRouter id also works directly.

Rules of thumb

• Report Codex's or the model's result to the user as that model's output, not yours; verify file changes Codex made before relying on them.
• Never send private or client material to :free or -contributor endpoints (the free chain, spark-cheap): their data policy allows logging. Paid endpoints only for that.
• Never start a second turn on a thread while one is running: it is silently merged into the active turn. Use steer for that.
• Compaction is lossy and emits no event; after long threads, restate the facts that matter.
• --sandbox danger-full-access and --approval never together give Codex unrestricted host access. Only with explicit user consent.
• Use --ephemeral for probes you do not want in the user's Codex history; otherwise clean up with delete. Plain calls are always ephemeral.
• Codex turns are billed against the user's ChatGPT plan (check limits before batch work); everything else goes through the OpenRouter key and shows in usage.

Providers and prompt size

--provider P uses [model_providers.P] from ~/.codex/config.toml (must be wire_api = "responses"; openrouter is configured and is implied by every alias and org/model id). --model a,b is a chain tried in order in fresh threads. --provider turns on --lean, which strips the connected ChatGPT-app tool schemas (490 KB -> ~48 KB per request); --instructions F or --system TEXT replaces Codex's built-in prompt with ours (15 KB); --plain adds the rest of what ask does (~7 KB). Details, measured sizes and the probed model table: references/findings.md.

Supporting files (load on demand)

• references/findings.md — verified behavior, gotchas, platform limits, timings (version-stamped).
• references/protocol.md — generated method/param reference for raw JSON-RPC work; regenerate with codexctl schema after codex update.
• scripts/codexrpc.js — the client library (CodexClient, runTurn) for custom orchestration beyond the CLI.
• assets/plain.md — the instructions plain calls use instead of Codex's agent prompt.